DETAILED ACTION
This action is responsive to Applicant Arguments and Remarks filed on June 17, 2026.
Claims 1, 11 and 18 have been amended. Claim 20 has been canceled. Claim 21 is new.
Notice of Pre-AIA or AIA Status
The present application, filed on or after March 16, 2013, is being examined under the first inventor to file provisions of the AIA .
Response to Amendment
Applicant's Remarks, filed June 17, 2026, has been fully considered and entered. Accordingly, Claims 1-19 and 21 are pending in this application. Claims 1, 11 and 18 have been amended. Claim 20 has been canceled. Claim 21 is new. Claims 1, 11 and 18 are independent claims.
Response to Arguments
Applicant’s arguments, see pages 8-11, filed June 17, 2026, with respect to the amendments of independent claims 1, 11 and 18 have been fully considered, but they are not persuasive.
Argument: Applicant argues that Thompson and Tiwari are silent as to categories themselves defined in terms of service API details, and that neither ties a similarity selected category to task the generation and API based invocation.
Response to Argument: Examiner respectfully disagrees. The Examiner respectfully points out that the test for obviousness is not that the claimed invention must be expressly suggested in any one or all of the references. Rather, the test is what the combined teachings of the references would have suggested to those of ordinary skill in the art. Obviousness can only be established by combining or modifying the teachings of the prior art to produce the claimed invention where there is some teaching, suggestion, or motivation to do so found either in the references themselves or in the knowledge generally available to one of ordinary skill in the art. See In re Fine, 837 F.2d 1071, 5 USPQ2d 1596 (Fed. Cir. 1988) and In re Jones, 958 F.2d 347, 21 USPQ2d 1941 (Fed. Cir. 1992).
One cannot show non-obviousness by attacking references individually where the rejections are based on combinations of references. See In re Keller, 642 F.2d 413,208 USPQ 871 (CCPA 1981); In re Merck & Co., Inc., 800 F.2d 1091, 231 USPQ 375 (Fed. Cir. 1986). See MPEP 2145. As set forth in the latest Non-Final office action, the combined teachings of Thompson-Tiwari would have suggested the claimed subject matter to those of ordinary skill in the art.
The mapped categories are Thompson’s skills and Thompson paragraph [0149] states that skills “can include services 434” and paragraph [0174] states that skills “are typically implemented as APIs”, and paragraph [0500] confirms that resources/services are accessed “by providing an application programming interface (API)”. This precisely maps to “each category is associated with one or more service application programming interface (API) details defining one or more services associated with the category”.
Thompson paragraphs [0360] disclose the use of a list of the agents available skills (skill section 1270), it compares the incoming request to that list and keeps the skill whose match is “machine-determined predicted or estimated degree of relevance, similarity… that satisfies (e.g., meets or exceeds) a threshold” Thus, by choosing the skill that satisfies the threshold it is selecting a category based on similarity comparison. Thompson paragraph [0429, 0435, 0315] further generates the plan/task based on intent and invokes the respective agent based on the selected skill. Thus, by using a skill it is invoking an API. Tiwari paragraphs [0021-0029, 0041-0042] describes a step that picks the best scoring category in more explicit similarity score language “comparing the query vector to the document vectors… to find the most relevant… determining… whether a relevance score… is above a confidence threshold level”. Thus, Tiwari uses intent classification to classify the intent of a particular question to a best category, and performs a similarity search comparing the query [e.g. user prompt] to the document vectors [e.g. categories] and computing a score for each document and select the highest relevant document to the query. Thus, Thompson-Tiwari teaches the argued-amended claim limitations. See rejection below.
Therefore, the Examiner has determined that this argument is not persuasive.
Claim Rejections - 35 USC § 101
35 U.S.C. 101 reads as follows:
Whoever invents or discovers any new and useful process, machine, manufacture, or composition of matter, or any new and useful improvement thereof, may obtain a patent therefor, subject to the conditions and requirements of this title.
Claims 1-5, 7-12, 14, 16-19 and 21 are rejected under 35 U.S.C. 101 because claimed invention is directed to an abstract idea without significantly more.
Step 1 analysis:
In the instant case, the claims are directed to a method (claims 1-10), computer readable medium (system 11-17), and computer -readable medium(claims 18-19 and 21). Thus, each of the claims falls within one of the four statutory categories (i.e. process, machine, manufacture of composition of matter).
Step2A analysis:
Based on determining the claim fall within or can be amended to fall within a statutory category (Step 1), it must be determined if the claims are directed to a judicial exception (i.e., law of nature, natural phenomenon, and abstract idea), in this case the claims fall within the judicial exception of an abstract idea. Specifically, the abstract idea of mental processes.
Step 2A: Prong One:
The claim(s) recite(s):
Claim 1 (Similarly in claims 11 and 18) :
“generating, by an intent classifier, similarity scores between the user prompt and categories corresponding to services accessible to the network component” recite an abstract idea as a mental process in the form of an evaluation. One can mentally evaluate similarity between a user prompt and categories and evaluate scores. Consistent with the specification in [0021] the BRI of a similarity score is to represent how related or relevant a category is to the user prompt. One can mentally evaluate such similarity or relevancy and evaluate scores (performed mentally or with the aid of pen and paper) between the user prompt and categories.
“determining an intent of the user prompt based upon contextual information of the user” recite an abstract idea as a mental process in the form of an evaluation or judgement or opinion. One can mentally judge the intent of a user prompt based on contextual information. Consistent with the specification in [0016] the BRI of such contextual information can be information related to a business of the user. One can mentally recognize such contextual information and evaluate or judge the intent of a user prompt.
“generating, by an artificial intelligence (AI) planner model, a task to perform based upon the intent and a category having a similarity score exceeding similarity scores of one or more other categories” recite an abstract idea as a mental process in the form of an evaluation or judgement. Consistent with the specification [0022] the BRI of such task are instructions to execute actions. One can mentally evaluate a similarity score exceeding similarity scores of one or more other categories in order to generate a task (performed mentally or with the aid of pen and paper).
“generating a response to the user prompt based upon the task result” recite an abstract idea as a mental process in the form of an evaluation or opinion. The claim does not impose any limits on how the response is generated, and consistent with the specification [0022] the BRI of such response is an indication based on a result. One can mentally generate a response or with the aid of pen and paper.
Step 2A: Prong Two:
The claim(s) recites the following additional elements:
“network component” is a high-level recitation of a generic computer components and represents mere instructions to apply on a computer as in MPEP 2106.05(f), which does not provide integration into a practical application.
“invoking an AI agent to perform the task, wherein the AI agent performs an application programming interface (API) call to a service, corresponding to the category, to retrieve a task result from the service” represents insignificant extra-solution activity as mere data gathering for the abstract idea (i.e. gathering task results for the generated task) and/or selecting a particular data source or type of data to be manipulated as identified in MPEP 2106.05(g) and does not provide integration into a practical application. The limitation provides no specifics as to what this task is nor what this result contains other than the result of the abstract idea determination.
The recitation of “intent classifier”, “artificial intelligence (AI) planner model” and “invoking an AI agent” merely indicates the field of use or technological environment in which the judicial exception is performed. This type of limitation merely confines the use of the abstract idea to a particular technological environment (AI models) and thus fails to add an inventive concept to the claims. See MPEP 2106.05(h).
“providing the response to the user” is insignificant extra-solution activity as mere data outputting as identified in MPEP 2106.05(g) and does not provide integration into a practical application. Adding a final step of "providing the response to the user" merely communicates the results, and adding such to a process that only recites determining whether a placement in a volume is appropriate (a mental process) does not add a meaningful limitation to the process of determining whether the placement is appropriate. Notably, the limitation provides no specifics as to what this response contains other than the result of the abstract idea determination.
In claims 11 and 18, “a system, comprising: one or more processors configured for executing instructions to perform operations” and “A non-transitory computer-readable medium storing instructions that when executed by one or more processors facilitate performance of operations” are a high-level recitation of a generic computer components and represents mere instructions to apply on a computer as in MPEP 2106.05(f), which does not provide integration into a practical application.
Viewing the additional limitations together and the claim as a whole, nothing provides integration into a practical application.
At Step 2B:
The conclusions for the mere implementation using a computer are carried over and does not provide significantly more.
With respect to retrieving the "task results for the generated task" identified as insignificant extra-solution activity above when re-evaluated this element is well-understood, routine, and conventional as evidenced by the court cases in MPEP 2106.05(d)(II), "i. Receiving or transmitting data over a network, e.g., using the Internet to gather data, Symantec, 838 F.3d at 1321, 120 USPQ2d at 1362 (utilizing an intermediary computer to forward information); … OIP Techs., Inc., v. Amazon.com, Inc., 788 F.3d 1359, 1363, 115 USPQ2d 1090, 1093 (Fed. Cir. 2015) (sending messages over a network); buySAFE, Inc. v. Google, Inc., 765 F.3d 1350, 1355, 112 USPQ2d 1093, 1096 (Fed. Cir. 2014) (computer receives and sends information over a network);" and thus remains insignificant extra-solution activity that does not provide significantly more.
With respect to the "providing the response to the user" identified as insignificant extra-solution activity above when re-evaluated this element is well-understood, routine, and conventional as evidenced by the court cases in MPEP 2106.05(d)(II), "i. Receiving or transmitting data over a network, e.g., using the Internet to gather data, Symantec, 838 F.3d at 1321, 120 USPQ2d at 1362 (utilizing an intermediary computer to forward information); … OIP Techs., Inc., v. Amazon.com, Inc., 788 F.3d 1359, 1363, 115 USPQ2d 1090, 1093 (Fed. Cir. 2015) (sending messages over a network); buySAFE, Inc. v. Google, Inc., 765 F.3d 1350, 1355, 112 USPQ2d 1093, 1096 (Fed. Cir. 2014) (computer receives and sends information over a network);" and thus remains insignificant extra-solution activity that does not provide significantly more.
Looking at the claim as a whole does not change this conclusion and the claim appears to be ineligible.
Further, regarding claim 2 the claim recites the same abstract idea and additional elements as identified in claim 1. Claim 2 further recites further abstract idea at Step 2A Prong One of:
“wherein the network component is a router” are a high-level recitation of a generic computer components and represents mere instructions to apply on a computer as in MPEP 2106.05(f), which does not provide integration into a practical application. Therefore, the additional elements recited in the claims do not integrate the abstract idea into a practical application because it does not impose any meaningful limits on practicing the abstract idea, thus failing to integrate the abstract idea into a practical application.
“extracting business information of a business associated with the user as the contextual information, wherein the user prompt relates to an aspect of the business.” This recites an abstract idea as a mental process in the form of an evaluation or judgement. One can mentally recognize such business information of a business associated with the user, and evaluate or judge it as contextual information. Claim 2 does not recite any other additional elements and for the same reasons as above with regard to integration into practical application and whether additional elements amount to significantly more, claim 2 also fails both Step 2A prong 2, thus the claim is directed to the judicial exception as it has not been integrated into practical application, and fails Step 2B as not amounting to significantly more. Therefore, claim 2 does not recite patent eligible subject matter under 35 U.S.C. § 101.
Further, regarding claim 3 it further recites additional elements that does not integrate the judicial exception into a practical application:
“wherein the AI planner model and the intent classifier are integrated into the router as an intent intelligence layer” this limitation merely confines the use of the abstract idea to a particular technological environment (AI models) and thus fails to add an inventive concept to the claims. See MPEP 2106.05(h). Further reciting generic computer components and represents mere instructions to apply on a computer as in MPEP 2106.05(f), which does not provide integration into a practical application. Claim 3 does not recite any other additional elements and for the same reasons as above with regard to integration into practical application and whether additional elements amount to significantly more, claim 3 also fails both Step 2A prong 2, thus the claim is directed to the judicial exception as it has not been integrated into practical application, and fails Step 2B as not amounting to significantly more. Therefore, claim 3 does not recite patent eligible subject matter under 35 U.S.C. § 101.
Further, regarding claim 4 it further recites additional elements that does not integrate the judicial exception into a practical application:
“wherein the AI planner model and the intent classifier are hosted by a device connected to the router” this limitation merely confines the use of the abstract idea to a particular technological environment (AI models) and thus fails to add an inventive concept to the claims. See MPEP 2106.05(h). Further reciting generic computer components and represents mere instructions to apply on a computer as in MPEP 2106.05(f), which does not provide integration into a practical application. Since claim 4 only merely confines the use of the abstract idea to a particular technological environment (AI models), claim 4 is also ineligible. Claim 4 does not recite any other additional elements and for the same reasons as above with regard to integration into practical application and whether additional elements amount to significantly more, claim 4 also fails both Step 2A prong 2, thus the claim is directed to the judicial exception as it has not been integrated into practical application, and fails Step 2B as not amounting to significantly more. Therefore, claim 4 does not recite patent eligible subject matter under 35 U.S.C. § 101.
Further, regarding claim 5, it further recites additional elements that does not integrate the judicial exception into a practical application:
“wherein the network component is a router connected to a device”, “invoking the service to control operation of the device” are a high-level recitation of a generic computer components and represents mere instructions to apply on a computer as in MPEP 2106.05(f), which does not provide integration into a practical application. Therefore, the additional elements recited in the claims do not integrate the abstract idea into a practical application because it does not impose any meaningful limits on practicing the abstract idea, thus failing to integrate the abstract idea into a practical application.
“utilizing machine learning functionality to process data collected from the device to generate the task result.” this limitation merely confines the use of the abstract idea to a particular technological environment (Machine learning) and thus fails to add an inventive concept to the claims. See MPEP 2106.05(h). Further reciting generic computer components and represents mere instructions to apply on a computer as in MPEP 2106.05(f), which does not provide integration into a practical application. Since claim 5 only merely confines the use of the abstract idea to a particular technological environment (Machine learning), claim 5 is also ineligible. There is nothing in Step 2B that provides significantly more, as there are no new additional elements in claim 5. Looking at the claim as a whole does not change this conclusion and the claim appears to be ineligible.
Further, regarding claim 7 (Similarly in claim 9 and 21) the claim recites the same abstract idea and additional elements as identified in claim 1. Claim 7 (Similarly in claim 9 and 21) further recites abstract idea at Step 2A Prong One of:
“generating, by the AI planner model, a plan to include a set of tasks to perform based upon the intent and one or more categories, wherein the set of tasks include the task” recite an abstract idea as a mental process in the form of an evaluation or judgement. One can mentally a plan to include a set of tasks (performed mentally or with the aid of pen and paper).
“for each task within the set of tasks, invoking a task specific AI agent assigned to perform a task of the set of tasks” this limitation merely confines the use of the abstract idea to a particular technological environment (AI models) and thus fails to add an inventive concept to the claims. See MPEP 2106.05(h).
“generating the response based upon task results returned by task specific AI agents created for the set of tasks.” recite an abstract idea as a mental process in the form of an evaluation or opinion. One can mentally generate a response or with the aid of pen and paper. The recitation of “AI agents” merely confines the use of the abstract idea to a particular technological environment (AI models) and thus fails to add an inventive concept to the claims. See MPEP 2106.05(h).
“utilizing machine learning functionality to process data collected from the device to generate the task result.” this limitation merely confines the use of the abstract idea to a particular technological environment (Machine learning) and thus fails to add an inventive concept to the claims. See MPEP 2106.05(h). Further reciting generic computer components and represents mere instructions to apply on a computer as in MPEP 2106.05(f), which does not provide integration into a practical application.
Also in claim 9 (similarly in claim 21) “evaluating the task result to determine whether the plan can continue to the next task or not” recite an abstract idea as a mental process in the form of an evaluation or judgement. One can mentally evaluate a result to determine whether a plan can continue to the next task or not.
Also in claim 9 (similarly in claim 21) “in response to determining that the plan cannot continue to the next task, generating a new plan with a new set of tasks.” recite an abstract idea as a mental process in the form of an evaluation or judgement. One can mentally evaluate a result to determine whether a plan cannot continue to the next task to generate a new plan (performed mentally or with the aid of pen and paper). Since claims 7, 9 and 21 only merely confines the use of the abstract idea to a particular technological environment (Machine learning), claims 7, 9 and 21 are also ineligible. Claims 7, 9 and 21 do not recite any other additional elements and for the same reasons as above with regard to integration into practical application and whether additional elements amount to significantly more, claims 7, 9 and 21 also fails both Step 2A prong 2, thus the claim is directed to the judicial exception as it has not been integrated into practical application, and fails Step 2B as not amounting to significantly more. Therefore, claims 7, 9 and 21 do not recite patent eligible subject matter under 35 U.S.C. § 101.
Further, regarding claim 8 the claim recites the same abstract idea and additional elements as identified in claim 1. Claim 8 further recites further abstract idea at Step 2A Prong One of:
“utilizing the service to monitor for an occurrence of an event” This recites an abstract idea as a mental process in the form of an evaluation or observation. One can mentally recognize such an occurrence of an event.
“in response to the event occurring, generating an alert for the user” recites an additional element “generating an alert”, which is an insignificant extra-solution activity (see MPEP 2106.05(g)), claim 8 also fails both Step 2A prong 2, thus the claim is directed to the judicial exception as it has not been integrated into practical application, and fails Step 2B as not amounting to significantly more. Therefore, Claim 8 does not recite patent eligible subject matter under 35 U.S.C. § 101. Looking at the claim as a whole does not change this conclusion and the claim appears to be ineligible.
Further, regarding claim 10 (similarly in claim 21) the claim recites the same abstract idea and additional elements as identified in claim 1. Claim 10 further recites only a further abstract idea at Step 2A Prong One of:
“in response to generating a threshold number of new plans, returning at least one of an error to the user or a request for clarification from the user.” recites additional element of generating a threshold number plans and returning at least one of an error or a request for clarification, which is an insignificant extra-solution activity (see MPEP 2106.05(g)). The additional elements is not sufficient to amount to significantly more than the judicial exception. Claims 10 and 21 also fails both Step 2A prong 2, thus the claim is directed to the judicial exception as it has not been integrated into practical application, and fails Step 2B as not amounting to significantly more. Therefore, Claims 10 and 21 do not recite patent eligible subject matter under 35 U.S.C. § 101. Looking at the claim as a whole does not change this conclusion and the claim appears to be ineligible.
Further, regarding claim 12 the claim recites the same abstract idea and additional elements as identified in claim 11. Claim 12 further recites an abstract idea at Step 2A Prong One of:
“generating a prompt vector to represent the user prompt; generating category vectors to represent the categories” The claim does not impose a limit on how the prompt vector and categories vectors are created. The plain meaning of a vector is a represent objects (e.g. as features), thus, this limitation recite an abstract idea as a mental process in the form of an evaluation or judgement. One can mentally evaluate a user prompt and categories to generate vectors (performed mentally or with the aid of pen and paper).
“comparing the prompt vector to the category vectors to generate the similarity scores.” recite an abstract idea as a mental process in the form of an evaluation or judgement. One can mentally evaluate vectors and compare features to decide which is most similar (performed mentally or with the aid of pen and paper). Since claim 12 only merely confines the use of the abstract idea to a particular technological environment (Machine learning), claims 12 are also ineligible. As stated in MPEP 2106.04(d)(III), "if the additional claim elements merely recite another judicial exception, that is insufficient to integrate the judicial exception into a practical application." There is nothing in Step 2B that provides significantly more, as there are no new additional elements in claims 12. Looking at the claim as a whole does not change this conclusion and the claim appears to be ineligible.
Further, regarding claim 14, it further recites the same abstract idea and additional elements identified in claim 11:
“invoking the service to automate execution of an action” is a high-level recitation of a generic computer components and represents mere instructions to apply on a computer as in MPEP 2106.05(f), which does not provide integration into a practical application. The additional elements recited in the claims cannot provide an inventive concept.
“populating the response with a result of executing the action.” recite an abstract idea as a mental process in the form of an evaluation or opinion. The claim does not impose any limits on how the response is generated, and consistent with the specification [0022] the BRI of such response is an indication based on a result. One can mentally generate a response or with the aid of pen and paper. The claims do not include additional elements that are sufficient to amount to significantly more than the judicial exception. Claim 14 does not recite any other additional elements and for the same reasons as above with regard to integration into practical application and whether additional elements amount to significantly more, claim 14 also fails both Step 2A prong 2, thus the claim is directed to the judicial exception as it has not been integrated into practical application, and fails Step 2B as not amounting to significantly more Therefore, Claim 14 does not recite patent eligible subject matter under 35 U.S.C. § 101.
Further, regarding claim 16 the claim recites the same abstract idea and additional elements as identified in claim 11. Claim 16 further recites only a further abstract idea at Step 2A Prong One of:
“defining the category for a messaging service used to transmit messages to user equipment.” This recites an abstract idea as a mental process in the form of an evaluation or judgement. One can mentally define a category for a service (or with the aid of pen and paper). Since claim 16 only adds an additional abstract idea to an already ineligible claim 11, claim 16 is also ineligible. As stated in MPEP 2106.04(d)(III), "if the additional claim elements merely recite another judicial exception, that is insufficient to integrate the judicial exception into a practical application." There is nothing in Step 2B that provides significantly more, as there are no new additional elements in claim 16. Looking at the claim as a whole does not change this conclusion and the claim appears to be ineligible.
Further, regarding claim 17 the claim recites the same abstract idea and additional elements as identified in claim 11. Claim 17 further recites only a further abstract idea at Step 2A Prong One of:
“defining the category for an event monitoring service used to monitor at least one of a data source, a sensor, or a device for occurrence of events.” This recites an abstract idea as a mental process in the form of an evaluation or judgement. One can mentally define a category for a service (or with the aid of pen and paper). Since claim 17 only adds an additional abstract idea to an already ineligible claim 11, claim 17 is also ineligible. As stated in MPEP 2106.04(d)(III), "if the additional claim elements merely recite another judicial exception, that is insufficient to integrate the judicial exception into a practical application." There is nothing in Step 2B that provides significantly more, as there are no new additional elements in claim 17. Looking at the claim as a whole does not change this conclusion and the claim appears to be ineligible.
Further, regarding claim 19 the claim recites the same abstract idea and additional elements as identified in claim 18. Claim 19 further recites only a further abstract idea at Step 2A Prong One of:
“defining the category for a fixed wireless access service used to execute router tasks for the network component.” This recites an abstract idea as a mental process in the form of an evaluation or judgement. One can mentally define a category for a service (or with the aid of pen and paper). Since claim 19 only adds an additional abstract idea to an already ineligible claim 18, claim 19 is also ineligible. As stated in MPEP 2106.04(d)(III), "if the additional claim elements merely recite another judicial exception, that is insufficient to integrate the judicial exception into a practical application." There is nothing in Step 2B that provides significantly more, as there are no new additional elements in claim 19. Looking at the claim as a whole does not change this conclusion and the claim appears to be ineligible.
Claim Rejections - 35 USC § 103
In the event the determination of the status of the application as subject to AIA 35 U.S.C. 102 and 103 (or as subject to pre-AIA 35 U.S.C. 102 and 103) is incorrect, any correction of the statutory basis (i.e., changing from AIA to pre-AIA ) for the rejection will not be considered a new ground of rejection if the prior art relied upon, and the rationale supporting the rejection, would be the same under either status.
The following is a quotation of 35 U.S.C. 103 which forms the basis for all obviousness rejections set forth in this Office action:
A patent for a claimed invention may not be obtained, notwithstanding that the claimed invention is not identically disclosed as set forth in section 102, if the differences between the claimed invention and the prior art are such that the claimed invention as a whole would have been obvious before the effective filing date of the claimed invention to a person having ordinary skill in the art to which the claimed invention pertains. Patentability shall not be negated by the manner in which the invention was made.
Claims 1-9 and 11-18 are rejected under 35 U.S.C. 103 as being unpatentable over Thompson (US Patent Application Publication No. US 20250371449 A1), in view of Tiwari (US Patent Application Publication No. US 20210133264 A1).
Regarding claim 1, Thompson teaches a method, comprising: receiving, by a network component, a user prompt from a user; (See Thompson [0068-0070, 0099, 0427-0428], discloses a computing system having automated agent components in communication with various elements, including a user device, a network, and/or one or more sensing devices, where the components are implemented using at least one application server or server cluster [e.g. network component], where responsive to receiving input [e.g. user prompt from a user] via one or more components, the automated agent can be dynamically configured to perform a series of tasks via multi-agent system.
Examiner notes that a server can be a network component.)
determining an intent of the user prompt based upon contextual information of the user; (See Thompson [0429] “At operation 1424, the processing device uses the at least one input [e.g. user prompt] to determine an objective [e.g. intent] of an entity [e.g. user]. For example, the processing device can use one or more machine learning models [e.g. intent classifier] to extract an objective from the at least one input or classify the at least one input as corresponding to a particular objective [Thus, determining an intent of the user prompt]. The objective can be a goal, intent, or task to be achieved via the use of an automated agent. Alternatively or in addition, an entity extraction process (e.g., a parser or named entity recognizer) can be used to extract one or more keywords or phrases [e.g. contextual information] from the at least one input [Thus, based upon contextual information of the user] and map the extracted words or phrases to a corresponding objective, goal, intent, or task.”)
generating, by an intent classifier, similarity scores between the user prompt and categories corresponding to services accessible to the network component; (See Thompson [0429-0430, 0450-0451] “ the processing device can use one or more machine learning models to extract an objective from the at least one input or classify the at least one input as corresponding to a particular objective…. For instance, a binary classification machine learning model [e.g. intent classifier] can be used… the processing device uses the objective to retrieve first context data… Context data can include data obtained using an entity identifier, such as an associated entity profile” See also Thompson [0535] “the machine learning model 1615 can be configured as a binary classifier or as a scoring model.” See also Thompson [0044, 0099] “The term entity may be used herein to refer to users… The input [e.g. user prompt] can include user input and/or context data”” See also Thompson [0174] “The agent can use skills to perform tasks… Skills can be assigned to agents via information extraction from a user profile” See also Thompson [0149] “The skills 423 can include services 434 [e.g. services accessible to the network component]… The skills 423 can also include… automated agent or sub-agents that can perform functions or actions” See also Thompson [0360] “A skill section 1270 identifies in more discrete terms skills [e.g. categories] that are associated with the automated agent… information extracted from the user's profile [e.g. user prompt] can be mapped or matched to the skills [e.g. category] shown in skill section 1270 using, e.g., a taxonomy or an embedding-based retrieval (EBR) technique. Match or map can refer to a machine-determined predicted or estimated degree of relevance, similarity [e.g. similarity score] or compatibility between entities or data items that satisfies (e.g., meets or exceeds) a threshold level of relevance, similarity or compatibility [Thus, similarity scores between the user prompt and categories corresponding to services accessible to the network component]”)
Thompson teaches the use of a machine learning model that can be a binary classifier or a scoring model, but does not explicitly disclose generating, by an intent classifier, similarity scores.
However, Tiwari teaches generating, by an intent classifier, similarity scores between the user prompt and categories corresponding to services accessible to the network component. (See Tiwari [0021-0029] “the disclosure may be practiced in network computing environments with many types of computer system configurations [e.g. network component]… FIG. 1 is a block diagram illustrating an environment 100… Any number of data sources 102 represent a corpus of data associated with a particular topic, product, service [e.g. services accessible to the network component], issue, and the like. Example data sources 102 include a knowledge base corpus 106, categories that are entered or extracted 108, categories made as intents 110… These items may be referred to herein as “documents”, “articles”… The category of a document can be… extracted automatically… a runtime 104 processes various questions [e.g. user prompt] 114 and requests from any number of users 116 via any type of interface 118… Run time 104 also includes vector space intent classification [e.g. intent classifier] to a best category 120 which attempts to classify the intent of a particular question 114. A text and vector space similarity search within a category 122 includes a text similarity search and/or a vector space similarity search. A text similarity search includes traditional information retrieval methods to search for the presence of words (or synonyms) in a given query. A vector space similarity search is performed by converting a query [e.g. user prompt] to a vector using sentence embedding during run time and comparing the query vector to the document vectors [e.g. categories] in the index (computed offline) to find the most relevant document to the query.” See also Tiwari [0041-0042] “The method 500 ranks 516 the identified (and relevant) articles and applies filters 518 to determine the best articles. The method 500 continues by determining 520 whether a relevance score [e.g. generating similarity scores] for each article is above a confidence threshold level… If at least one article is determined to be above the confidence threshold level, then the top article (e.g., the highest ranked article) is returned 524 to the user.”)
It would have been obvious to someone of ordinary skill in the art before the effective filing date of the claimed invention to modify Thompson; which uses a machine learning model that can be a binary classifier or a scoring model to classify user input as an objective/intent to be achieved via the use of an automated agent, and uses information obtained from the input [e.g. user prompt] to assign skills [e.g. categories] to the agent by determining [thus, generating] a degree of similarity [e.g. similarity score] between information obtained from the input and skills that satisfies a threshold level of similarity, to incorporate the teachings of Tiwari which uses a intent classification to classify the intent of a particular question 114 to a best category, and performs a similarity search comparing the query [e.g. user prompt] to the document vectors [e.g. categories] and computing a score for each document and select the highest relevant document to the query.”
One would be motivated to do so to enhance accuracy and relevance of results.
Thompson further in view of Tiwari, [hereinafter Thompson-Tiwari] additionally disclose wherein each category is associated with one or more service application programming interface (API) details defining one or more services associated with the category; (See Thompson [0149, 0174] “The skills 423 [e.g. categories] can include services 434, which can be external or third-party modules that can provide additional functionalities or capabilities [e.g. each category is associated with / defines one or more services associated with the category]… Skills are typically implemented as APIs, data systems, other agents… The agent can use skills to perform tasks that require external interaction, such as… invoking an API [Thus, each category is associated with one or more service API details]” See also Thompson [0500] “Each data resource or tool 1550 enables an agent… to access the data resource or tool… by providing an application programming interface (API)… a data resource or tool 1550 can provide a set of APIs [e.g. the service API details of the category]”)
generating, by an artificial intelligence (AI) planner model, a task to perform based upon the intent and a category determined to have a similarity score exceeding similarity scores of one or more other categories; (See Thompson [0074, 0080, 0108] “Any sub-agent 106A, 106B, . . . , 106N can include… AI services 114, data resources 116… configured to perform a specific task or action. For example, a sub-agent 106A, 106B, . . . , 106N can have or include an associated an associated agent profile, planner [e.g. an artificial intelligence (AI) planner model]… Task or action as used herein can refer to an atomic action or operation that an agent or sub-agent is configured to perform … a planner sub-agent of the distributed multi-agent system 105 can be invoked to generate a plan for responding to the input… The plan can include a plurality of actions [Thus, generating a task to perform] that need to be performed” See also Thompson [0435] “At operation 1436, the processing device uses the context-configured prompt and a machine learning model to generate a first plan comprising one or more tasks executable by at least the automated agent to complete the objective. [Thus, generating, by an artificial intelligence (AI) planner model, a task to perform based upon the intent]” See also Thompson [0174, 0323, 0360] “The agent can use skills to perform tasks… Skills can be assigned to agents via information extraction from a user profile… A skill section 1270 identifies in more discrete terms skills [e.g. categories] that are associated with the automated agent… information extracted from the user's profile can be mapped or matched to the skills [e.g. category] shown in skill section 1270 using, e.g., a taxonomy or an embedding-based retrieval (EBR) technique. Match or map can refer to a machine-determined predicted or estimated degree of relevance, similarity [e.g. similarity score] or compatibility between entities or data items that satisfies (e.g., meets or exceeds) a threshold level of relevance, similarity or compatibility [Thus, based upon a category determined to have a similarity score exceeding similarity scores of one or more other categories]”)
invoking an AI agent to perform the task, wherein the AI agent performs an API call to a service, corresponding to the category, to retrieve a task result from the service; (See Thompson [0108, 0174] “To generate a response to the input, the automated agent 102 can invoke one or more sub-agents [e.g. AI agent] of the distributed multi-agent system 105… Skills as used herein include complex, remote operations that the agent can perform. Skills are typically implemented as APIs, data systems, other agents, groups or teams of agents, or human users. The agent can use skills [e.g. corresponding to the category] to perform tasks that require external interaction, such as querying a database, invoking an API” See also Thompson [0315] “The execution process can perform the task or the sub-tasks using one or more context models, workflows, the planners, agents, tools, Als, or any other component, service, or resource available to the automated agent. The execution process includes… an action invocation function… The action invocation function applies… API calls to their respective targets (e.g., LLMs, models, tools, or other resources), thereby invoking the respective action agents. [Thus, invoking an AI agent to perform the task, wherein the AI agent performs an API call to a service, corresponding to the category, to retrieve a task result from the service]”)
generating a response to the user prompt based upon the task result; and providing the response to the user. (See Thompson [0076] “After the automated agent 102 performs a task and/or presents output to the user, based on the predictions obtained from the Bayesian model, the automated agent 102 monitors the user's actual response to the task performed and output produced by the automated agent 102 [Thus, generating a response to the user prompt based upon the task result, and providing the response to the user].”)
Regarding claim 2, Thompson-Tiwari teaches all limitations and motivations of claim 1, wherein the network component is a router, and wherein the method further comprises: extracting business information of a business associated with the user as the contextual information, wherein the user prompt relates to an aspect of the business. (See Thompson [0110, 0121] “FIG. 2 is a flow diagram of an example method for creating and configuring an automated agent using components of an agent system… the method is performed by components of distributed multi-agent system 105 [e.g. network component], including, in some embodiments, components or flows shown in FIG. 2… The automated agent 212 is defined by and includes an agent definition 214, adaptive machine learning-based orchestrator 216, multi-layer memory 218, router 222 [Thus, the network component is a router]” See also Thompson [0409-0411] “At operation 1404, the processing device uses the at least one input [e.g. user prompt] to determine an entity identity [e.g. business information of a business associated with the user as the contextual information]. For example, the processing device extracts a unique identifier, such as a user identifier, group identifier, entity identifier, session identifier, device identifier, or network address, from the at least one input… At operation 1406, the processing device uses the entity identity to create an automated agent and load context data associated with the entity identity into at least one layer of a multi-layer memory of the automated agent… Context data can include data obtained using the entity identifier, such as an associated entity profile” See also Thompson [0105] “Entity profile data can include example, current and/or historical attribute data associated with the user (e.g., user preferences and/or biographical data such as skills, work experiences, and education history) or another entity associated with the user (such as a company or a computing resource) [Thus, business information of a business associated with the user as the contextual information].” See also Thompson [0044] “The term entity may be used herein to refer to users and/or to other types of entities, such as companies, organizations, institutions, associations, cohorts, or groups of entities.”)
Regarding claim 3, Thompson-Tiwari teaches all limitations and motivations of claim 2, wherein the AI planner model and the intent classifier are integrated into the router as an intent intelligence layer. (See Thompson [0121] “The automated agent 212 is defined by and includes… router 222 [e.g. the router] ” See also Thompson [0092] “The AI services 114 can include various types of machine learning models and/or algorithms… that can provide, for instance… intent classification [e.g. intent classifier]”See also Thompson [0073-0074, 0079] “ Each sub-agent 106A, 106B, . . . , 106N can have a specific role or function, such as… a planner [e.g. AI planner model] … Any sub-agent 106A, 106B, . . . , 106N can include or be defined by a combination of computer code, data, memory, AI services 114 [e.g. intent classifier]… Embodiments of the automated agent 102 or any sub-agent 106A, 106B, . . . , 106N include semi-autonomous cognitive artificial intelligence that learns through interactions with human users [e.g. integrated into the router as an intent intelligence layer]”
Examiner notes that based on the Specification paragraph [0015] “The disclosed intent intelligence layer improves the operation and functionality of network components by integrating new artificial intelligence (AI) functionality into network components that may otherwise lack such functionality.” The term “intent intelligence layer” refers to integrating artificial intelligence (AI) functionality into network components)
Regarding claim 4, Thompson-Tiwari teaches all limitations and motivations of claim 2, wherein the AI planner model and the intent classifier are hosted by a device connected to the router. (See Thompson [0068-0070]” The automated agent 102 is in communication with various elements of an environment 101, including a user device 101A, a network 101B [Thus, connected], and/or one or more sensing devices 101C… the components of the computing system 100 are implemented using at least one application server or server cluster [Thus, hosted by a device (e.g. connected to the router)]” See also Thompson [0110] “FIG. 2 is a flow diagram of an example method for creating and configuring an automated agent using components of an agent system… the method is performed by components of distributed multi-agent system 105, including… components or flows shown in FIG. 2 [e.g. router]” See also Thompson [0121] “The automated agent 212 is defined by and includes… router 222 [e.g. the router] ” See also Thompson [0092] “The AI services 114 can include various types of machine learning models and/or algorithms… that can provide, for instance… intent classification [e.g. intent classifier]”See also Thompson [0073-0074, 0079] “ Each sub-agent 106A, 106B, . . . , 106N can have a specific role or function, such as… a planner [e.g. AI planner model] … Any sub-agent 106A, 106B, . . . , 106N can include or be defined by a combination of computer code, data, memory, AI services 114 [e.g. intent classifier]… Embodiments of the automated agent 102 or any sub-agent 106A, 106B, . . . , 106N include semi-autonomous cognitive artificial intelligence that learns through interactions with human users” Thus, the AI planner model and the intent classifier are hosted by a device connected to the router.)
Regarding claim 5, Thompson-Tiwari teaches all limitations and motivations of claim 1, wherein the network component is a router connected to a device, and wherein the method further comprises: invoking the service to control operation of the device; and (See Thompson [0070-0072] “the components of the computing system 100 are implemented using at least one application server or server cluster [e.g. network component, device]… the distributed multi-agent system 105 includes a plurality of sub-agents 106A, 106B, . . . , 106N, a communication service 108, an adaptive machine learning service 110, and a multi-layer memory structure 111. Any reference to N herein can refer to an Nth element of a device” See also Thompson [0110-0111] “FIG. 2 is a flow diagram of an example method for creating and configuring an automated agent using… components of distributed multi-agent system 105” See also Thompson [0593-0594] “ In FIG. 17 , an example machine… corresponds to a portion of computing system 100… when the computing system is executing a portion of automated agent 102 or distributed multi-agent system 105... The machine [e.g. network component, router] is connected (e.g., networked) to other machines in a network [e.g. device], such as a local area network (LAN), an intranet, an extranet, and/or the Internet. The machine can operate in the capacity of a server” See also Thompson [0121-0122] “The automated agent 212 is defined by and includes an agent definition 214, adaptive machine learning-based orchestrator 216, multi-layer memory 218, router 222 [Thus, is a router]… Adaptive machine learning-based orchestrator 216 includes or invokes an AI service 230, such as an LLM, that controls the operation of the automated agent 212 [Thus, invoking the service to control operation of the device]”)
utilizing machine learning functionality to process data collected from the device to generate the task result. (See Thompson [0086] “A context model can include various types of information, such as preferences, policies, profile data, historical user activity data, sensor data, network data, model parameters, and/or any other data that can be used to configure an agent, workflow, plan, or task [e.g. to generate the task result]…. In the case of physical devices that are controlled by the automated agent 102, a context model can include sensor data collected via sensors associated with the physical devices [Thus, process data collected from the device to generate the task result]” See Thompson [0131, 0135] “At block 306, the adaptive machine learning-based orchestrator 216 can load a workflow to accomplish the objective given the event and the context model. The workflow can be a sequence or an arrangement of steps, tasks, actions, or functions that the automated agent can perform (alone and/or via delegation to one or more sub-agents) to achieve the objective…. the adaptive machine learning-based orchestrator 216 can determine whether the objective has been achieved based on the event, the context model [Thus, utilizing machine learning functionality to process data collected from the device], the plan, and/or the result of the action.”)
Regarding claim 6, Thompson-Tiwari teaches all limitations and motivations of claim 1, further comprising: dynamically generating the AI agent based upon the task; and (See Thompson [0205] “An automated agent [e.g. AI agent] may refer to an agent that is dynamically configured to perform one or more tasks [Thus, dynamically generating the AI agent based upon the task]”)
inputting service API details for the category into the AI agent for creating the API call. (See Thompson [0108, 0174] “To generate a response to the input, the automated agent 102 can invoke one or more sub-agents [e.g. AI agent] of the distributed multi-agent system 105… Skills as used herein include complex, remote operations that the agent can perform. Skills [e.g. category] are typically implemented as APIs, data systems, other agents, groups or teams of agents, or human users. The agent can use skills [e.g. corresponding to the category] to perform tasks that require external interaction, such as querying a database, invoking an API [Thus, inputting service API details for the category into the AI agent]” See also Thompson [0315] “The execution process includes… an action invocation function… The action invocation function applies… API calls to their respective targets (e.g., LLMs, models, tools, or other resources), thereby invoking the respective action agents. [Thus, for creating the API call]”)
Regarding claim 7, Thompson-Tiwari teaches all limitations and motivations of claim 1, further comprising: generating, by the AI planner model, a plan to include a set of tasks to perform based upon the intent and one or more categories, wherein the set of tasks include the task; (See Thompson [0132-0133] “At block 308, the adaptive machine learning-based orchestrator 216 can invoke a planner to use the workflow to generate or update a plan to accomplish the objective [Thus, include the task]. The planner can be an AI service 230, such as an LLM [e.g. AI planner model], which can create, modify, or optimize a plan based on the workflow, the event, the context model, and/or feedback from the user or another agent. The plan can be a representation of the workflow that specifies… tasks [Thus, generating a plan to include a set of tasks to perform]… At block 310, the adaptive machine learning-based orchestrator 216 can… initiate or cause execution of one or more actions from the plan [e.g. set of tasks include the task] via one or more agents, using a portion of the context model associated with the action [Thus, based upon the intent and one or more categories]. The action can be a step, a task, a function, or a communication that the automated agent 212 can perform” See also Thompson [0325] “The skill-based agents 1102 and/or agents of the agent team 1104 can be classified into different types or categories [Thus, based on one or more categories] based on their skills, functions, roles, or responsibilities. Each of these agents can have different skills that can be used to perform different actions or portions of actions.”)
for each task within the set of tasks, invoking a task specific AI agent assigned to perform a task of the set of tasks; and (See Thompson [0108, 0160, 0167] “To generate a response to the input, the automated agent 102 can invoke one or more sub-agents [e.g. AI agent] of the distributed multi-agent system 105… where each action has an associated action sub-agent… Each task is or includes an action… Actions (e.g., tools or skills) can be performed, for example, by a planner agent explicitly or through an LLM selection. For instance, a tool can be configured to, given some context (e.g., task to be performed), select and invoke the most appropriate action [Thus, for each task within the set of tasks]” See also Thompson [0073-0074] “ Each sub-agent 106A, 106B, . . . , 106N can have a specific role or function, such as a profile sub-agent, a planner sub-agent, a workflow sub-agent, a memory sub-agent, or any other sub-agent that can assist the automated agent 102 in executing tasks… Any sub-agent 106A, 106B, . . . , 106N [e.g. AI agent] can include or be defined by… AI services… configured to perform a specific task or action [Thus, task specific AI agent assigned to perform a task of the set of tasks].”)
generating the response based upon task results returned by task specific AI agents created for the set of tasks. (See Thompson [0076] “After the automated agent 102 performs a task and/or presents output to the user, based on the predictions obtained from the Bayesian model, the automated agent 102 monitors the user's actual response to the task performed and output produced by the automated agent 102 [Thus, generating a response based upon task results returned by task specific AI agents created for the set of tasks].”)
Regarding claim 8, Thompson-Tiwari teaches all limitations and motivations of claim 1, further comprising: utilizing the service to monitor for an occurrence of an event; and (See Thompson [0129] “In FIG. 3 , the method 300 can be performed, for example, by the adaptive machine learning-based orchestrator 216 in coordination with the multi-layer memory 218, the messaging service 210, the AI services 230, the tools 232, the data resources 226, and/or any other component or service of the automated agent system 212. The method can begin at block 302, where an event is detected. [Thus, utilizing the service to monitor for an occurrence of an event]”)
in response to the event occurring, generating an alert for the user. (See Thompson [0130-0134] “ At block 304, the adaptive machine learning-based orchestrator 216 can obtain a context model… and determine an objective for the automated agent based on the event [Thus, in response to the event occurring] and the context model… At block 306, the adaptive machine learning-based orchestrator 216 can load a workflow to accomplish the objective given the event and the context model. The workflow can be a sequence or an arrangement of… actions, or functions that the automated agent can perform (alone and/or via delegation to one or more sub-agents) to achieve the objective… The adaptive machine learning-based orchestrator 216 can use the messaging service 210 to send or receive messages [e.g. alert] to or from the user [Thus, for the user] (e.g., messages that contain or reference input, context data, feedback, etc.)… to perform or delegate one or more actions [Thus, in response to the event occurring] and/or to monitor the performance of actions and/or the plan as a whole.”)
Regarding claim 9, Thompson-Tiwari teaches all limitations and motivations of claim 1, further comprising: generating, by the AI planner model, a plan to include a set of tasks to perform based upon the intent and one or more categories, wherein the set of tasks include the task; (See Thompson [0132-0133] “At block 308, the adaptive machine learning-based orchestrator 216 can invoke a planner to use the workflow to generate or update a plan to accomplish the objective [Thus, include the task]. The planner can be an AI service 230, such as an LLM [e.g. AI planner model], which can create, modify, or optimize a plan based on the workflow, the event, the context model, and/or feedback from the user or another agent. The plan can be a representation of the workflow that specifies… tasks [Thus, generating a plan to include a set of tasks to perform]… At block 310, the adaptive machine learning-based orchestrator 216 can… initiate or cause execution of one or more actions from the plan [e.g. set of tasks include the task] via one or more agents, using a portion of the context model associated with the action [Thus, based upon the intent and one or more categories]. The action can be a step, a task, a function, or a communication that the automated agent 212 can perform” See also Thompson [0325] “The skill-based agents 1102 and/or agents of the agent team 1104 can be classified into different types or categories [Thus, based on one or more categories] based on their skills, functions, roles, or responsibilities. Each of these agents can have different skills that can be used to perform different actions or portions of actions.”)
evaluating the task result to determine whether the plan can continue to the next task or not; and in response to determining that the plan cannot continue to the next task, generating a new plan with a new set of tasks. (See Thompson [0132-0139] “ At block 308, the adaptive machine learning-based orchestrator 216 can invoke a planner to use the workflow to generate or update a plan to accomplish the objective… The plan… specifies the order, timing, frequency, duration, priority, or concurrency of the steps, tasks, actions… At block 312… can determine whether the objective has been achieved based on the event, the context model, the plan, and/or the result of the action [Thus, evaluating the task result]… the objective can include multiple sub-objectives, e.g., an objective associated with each sub-action or sub-task… If at block 312 it is determined that the objective and/or sub-objectives has not been achieved… orchestrator 216 initiates another action from the plan using a portion of the context model associated with the action… If at block 316 it is determined that the action has not been achieved… orchestrator 216 can update the portion of the context model used to perform the action (e.g., to adjust the weights applied to different portions of the context model) and then return to block 304 to obtain the updated context model and re-determine the objective [Thus, in response to determining that the plan cannot continue to the next task]… the orchestrator 216 can regenerate or update the plan [e.g. generating a new plan with a new set of tasks] at block 308 and begin executing actions of the regenerated or updated plan at block 310.”)
Regarding claim 11, Thompson-Tiwari teaches all of the elements of claim 1 in method form rather than system form. Therefore, the supporting rationale of the rejection to claim 1 applies equally as well to those elements of claim 11.
Regarding claim 12, Thompson-Tiwari teaches all of the elements of claim 1 in method form rather than system form. Therefore, the supporting rationale of the rejection to claim 1 applies equally as well to those elements of claim 12.
Regarding claim 13, Thompson-Tiwari teaches all limitations and motivations of claim 11, wherein the operations further comprise: executing an action as the response, wherein the action includes at least one automated scheduler manager functionality. (See Thompson [0205, 0232] “Agent as used herein can refer to an automated agent… The orchestration agent 506 can be an agent that can coordinate, schedule, or execute the actions involved in the plans.” See also Thompson [0311-0314] “a task decomposition process 1000 includes… a decomposition sub-process 1006… the task decomposition process 1000 can be performed in response to receipt by an agent or another component of the computing system (e.g., computing system 100) of a message 1002. The message 1002 can be or include input, such as data or one or more signals, from a user [e.g. user prompt]… The message 1002 can include… any other type of communication that explicitly or implicitly initiates, modifies, or terminates a task or an action [e.g. executing an action as the response]… The decomposition process 1006 includes... a sub-task scheduling function [e.g. automated scheduler manager functionality]… The sub-task scheduling function can assign or delegate sub-task to respective agents or tools”)
Regarding claim 14, Thompson-Tiwari teaches all limitations and motivations of claim 11, wherein the operations further comprise: invoking the service to automate execution of an action; and populating the response with a result of executing the action. (See Thompson [0076, 0121-0122] “After the automated agent 102 performs a task [e.g. action] and/or presents output [Thus, populating the response with a result of executing the action] to the user, based on the predictions obtained from the Bayesian model, the automated agent 102 monitors the user's actual response to the task performed and output produced by the automated agent 102… The automated agent 212 is defined by and includes an agent definition 214, adaptive machine learning-based orchestrator 216… Adaptive machine learning-based orchestrator 216 includes or invokes an AI service 230 [e.g. invoking the service], such as an LLM, that controls the operation of the automated agent 212 [Thus, to automate execution of an action]”)
Regarding claim 15, Thompson-Tiwari teaches all limitations and motivations of claim 11, wherein the operations further comprise: receiving, over a network from a service provider, an update for dynamically updating service API details used by the AI agent to generate the API call to the service, wherein the update is applied to modify a file used by the network component to store the service API details. (See Thompson [0599-0600] “agent system 1750 represents portions of automated agent 102 or distributed multi-agent system 105 [e.g. used by the network component]… The network link can provide data communication through at least one network to other data devices… provide a connection to… the “Internet”… by an Internet Service Provider (ISP) [Thus, over a network from a service provider]” See also Thompson [0103, 0106] “The data resources 116 can include various types of data or information that can be used by the automated agent 102, any sub-agent 106A, 106B, . . . , 106N, any of the AI services 114, or any of the tools 118. Examples of data resources 116 include but are not limited to workflows 116A, registries 116B, data stores 116C… Registries 116B can include, e.g., files… that store information about [e.g. API details] which AI services 114, data resources 116, and/or tools 118 [Thus, a file used by the network component to store the service API details] are accessible to a particular automated agent 102 [Thus, used by the AI agent]… Examples of tools 118 include but are not limited to application programming interfaces (APIs) 118A [e.g. used by the AI agent to generate the API call]” See also Thompson [0120] “the contents of the registries is dynamic and can be changed or updated [Thus, dynamically updating] from one instance to the next”)
Regarding claim 16, Thompson-Tiwari teaches all limitations and motivations of claim 11, wherein the operations further comprise: defining the category for a messaging service used to transmit messages to user equipment. (See Thompson [0073-0074] “ Each sub-agent 106A, 106B, . . . , 106N can have a specific role or function [e.g. category for messaging service] … Any sub-agent 106A, 106B, . . . , 106N can include or be defined [Thus, defining] by a combination of computer code, data, memory, AI services 114, data resources 116, and/or tools 118 [e.g. messaging service], which are arranged or configured to perform a specific task or action.” See also Thompson [0325] “The skill-based agents 1102 and/or agents of the agent team 1104 can be classified into different types or categories [e.g. category for an event monitoring service] based on their skills, functions, roles, or responsibilities.” See also Thompson [0134] “The adaptive machine learning-based orchestrator 216 can use the messaging service 210 to send or receive messages to or from the user [Thus, a messaging service used to transmit messages to user equipment]”)
Regarding claim 17, Thompson-Tiwari teaches all limitations and motivations of claim 11, wherein the operations further comprise: defining the category for an event monitoring service used to monitor at least one of a data source, a sensor, or a device for occurrence of events. (See Thompson [0073-0074] “ Each sub-agent 106A, 106B, . . . , 106N can have a specific role or function [e.g. category for event monitoring service] … Any sub-agent 106A, 106B, . . . , 106N can include or be defined [Thus, defining] by a combination of computer code, data, memory, AI services 114, data resources 116, and/or tools 118 [e.g. event monitoring service], which are arranged or configured to perform a specific task or action.” See also Thompson [0325] “The skill-based agents 1102 and/or agents of the agent team 1104 can be classified into different types or categories [e.g. category for an event monitoring service] based on their skills, functions, roles, or responsibilities. See also Thompson [0500, 0513] “Data resources and tools 1550 include computing resources, such as data stores… Each data resource or tool 1550 can include a monitoring service [e.g. category for an event monitoring service] that periodically generates, publishes, or broadcasts availability [e.g. event] and/or other performance metrics associated with the data resource [Thus, monitor at least one of a data source]… application system 1530… agent system 1580… are shown as separate elements in FIG. 15 for case of discussion but, except as otherwise described, the illustration is not meant to imply that separation of these elements is required.”
Thus, the agents are classified into different types or categories based on their skills, functions, roles, or responsibilities, and are defined by data resources and/or tools to perform a specific task or action, where each data resource or tool can include a monitoring service (e.g. category for an event monitoring service). Thus, the monitoring service broadcasts availability (e.g. event) and generate performance metrics associated with the data resource.)
Regarding claim 18, Thompson-Tiwari teaches all of the elements of claim 1 in method form rather than computer readable medium form. Therefore, the supporting rationale of the rejection to claim 1 applies equally as well to those elements of claim 18.
Allowable Subject Matter
Claims 10, 19 and 21 are objected to as being dependent upon a rejected base claim, but would be allowable if rewritten in independent form including all of the limitations of the base and any intervening claims. After sufficient search and analysis, Examiner concluded that the claimed invention has been recited in such a manner that dependent claims 10, 19 and 21 are not taught by any prior reference found through search. The primary reason for allowance of the claims in this case, is the inclusion of the limitations “in response to generating a threshold number of new plans, returning at least one of an error to the user or a request for clarification from the user.”, and “wherein the operations further comprise: defining the category for a fixed wireless access service used to execute router tasks for the network component.” which are not found in the prior art of record. Overcoming the 101 rejection and incorporating claims 10, 19 and 21 in independent form including all of the limitations of the base and any intervening claims would put claims in condition for allowance.
Conclusion
THIS ACTION IS MADE FINAL. Applicant is reminded of the extension of time policy as set forth in 37 CFR 1.136(a).
A shortened statutory period for reply to this final action is set to expire THREE MONTHS from the mailing date of this action. In the event a first reply is filed within TWO MONTHS of the mailing date of this final action and the advisory action is not mailed until after the end of the THREE-MONTH shortened statutory period, then the shortened statutory period will expire on the date the advisory action is mailed, and any nonprovisional extension fee (37 CFR 1.17(a)) pursuant to 37 CFR 1.136(a) will be calculated from the mailing date of the advisory action. In no event, however, will the statutory period for reply expire later than SIX MONTHS from the mailing date of this final action.
Any inquiry concerning this communication or earlier communications from the examiner should be directed to OSCAR WEHOVZ whose telephone number is (571)272-3362. The examiner can normally be reached 8:00am - 5:00pm ET.
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/OSCAR WEHOVZ/Examiner, Art Unit 2161
/APU M MOFIZ/Supervisory Patent Examiner, Art Unit 2161
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